Evidence map›Paper›PMID 42419258›Full record

ArticleCell genomics2026

When network biology meets human genetics.

Sai Zhang

Abstract readComment
In one paragraph

Article in Cell genomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Sai ZhangDepartment of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT 06510, USA. Electronic address: sai.zhang@yale.edu.

Funding

Deep Learning for Single-Cell GeneticsR35GM157219 · NIGMS · YALE UNIVERSITY · PI Sai Zhang · 2025 to 2026
$797k
NIGMS NIH HHS R35 GM157219
6 · The paper itself

Abstract

Rare variant association analyses are typically performed at the single-gene level, overlooking the molecular interactions that organize cellular systems. In this issue, Nazeen et al. introduce NERINE, a probabilistic rare variant burden test that integrates gene and protein networks to improve statistical power and biological interpretability.

Indexed as

Gene Regulatory NetworksHuman GeneticsHumans

Identifiers

PMID42419258
PMCPMC13347932

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.